A novel two-layer SVM model in miRNA Drosha processing site detection

Persistent Link:
http://hdl.handle.net/10150/610104
Title:
A novel two-layer SVM model in miRNA Drosha processing site detection
Author:
Hu, Xingchi; Ma, Chuang; Zhou, Yanhong
Affiliation:
Hubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China; School of Plant Sciences, University of Arizona, Tucson, AZ, USA
Issue Date:
2013
Publisher:
BioMed Central
Citation:
Hu et al. BMC Systems Biology 2013, 7(Suppl 4):S4 http://www.biomedcentral.com/1752-0509/7/S4/S4
Journal:
BMC Systems Biology
Rights:
© 2013 Hu et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
Collection Information:
This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
Abstract:
BACKGROUND:MicroRNAs (miRNAs) are a large class of non-coding RNAs with important functions wide spread in animals, plants and viruses. Studies showed that an RNase III family member called Drosha recognizes most miRNAs, initiates their processing and determines the mature miRNAs. The Drosha processing sites identification will shed some light on both miRNA identification and understanding the mechanism of Drosha processing.METHODS:We developed a computational method for Drosha processing site predicting, named as DroshaPSP, which employs a two-layer mathematical model to integrate structure feature in the first layer and sequence features in the second layer. The performance of DroshaPSP was estimated by 5-fold cross-validation and measured by ACC (accuracy), Sn (sensitivity), Sp (specificity), P (precision) and MCC (Matthews correlation coefficient).RESULTS:The results of testing DroshaPSP on the miRNA data of Drosophila melanogaster indicated that the Sn, Sp, and MCC thereof reach to 0.86, 0.99 and 0.86 respectively.CONCLUSIONS:We found the Shannon entropy, a chemical kinetics feature, is a significant feature in telling the true sites among the nearby sites and improving the performance.
EISSN:
1752-0509
DOI:
10.1186/1752-0509-7-S4-S4
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1752-0509/7/S4/S4

Full metadata record

DC FieldValue Language
dc.contributor.authorHu, Xingchien
dc.contributor.authorMa, Chuangen
dc.contributor.authorZhou, Yanhongen
dc.date.accessioned2016-05-20T08:58:40Z-
dc.date.available2016-05-20T08:58:40Z-
dc.date.issued2013en
dc.identifier.citationHu et al. BMC Systems Biology 2013, 7(Suppl 4):S4 http://www.biomedcentral.com/1752-0509/7/S4/S4en
dc.identifier.doi10.1186/1752-0509-7-S4-S4en
dc.identifier.urihttp://hdl.handle.net/10150/610104-
dc.description.abstractBACKGROUND:MicroRNAs (miRNAs) are a large class of non-coding RNAs with important functions wide spread in animals, plants and viruses. Studies showed that an RNase III family member called Drosha recognizes most miRNAs, initiates their processing and determines the mature miRNAs. The Drosha processing sites identification will shed some light on both miRNA identification and understanding the mechanism of Drosha processing.METHODS:We developed a computational method for Drosha processing site predicting, named as DroshaPSP, which employs a two-layer mathematical model to integrate structure feature in the first layer and sequence features in the second layer. The performance of DroshaPSP was estimated by 5-fold cross-validation and measured by ACC (accuracy), Sn (sensitivity), Sp (specificity), P (precision) and MCC (Matthews correlation coefficient).RESULTS:The results of testing DroshaPSP on the miRNA data of Drosophila melanogaster indicated that the Sn, Sp, and MCC thereof reach to 0.86, 0.99 and 0.86 respectively.CONCLUSIONS:We found the Shannon entropy, a chemical kinetics feature, is a significant feature in telling the true sites among the nearby sites and improving the performance.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1752-0509/7/S4/S4en
dc.rights© 2013 Hu et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)en
dc.titleA novel two-layer SVM model in miRNA Drosha processing site detectionen
dc.typeArticleen
dc.identifier.eissn1752-0509en
dc.contributor.departmentHubei Bioinformatics and Molecular Imaging Key Laboratory, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Chinaen
dc.contributor.departmentSchool of Plant Sciences, University of Arizona, Tucson, AZ, USAen
dc.identifier.journalBMC Systems Biologyen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.